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Innovative Methodologies and
Technologies: The PREDICT Programme
Dr Sarah Berry
King’s College LondonDept. Nutritional Sciences
Qualit
y &
Pre
cis
ion
Quantity
PREDICT within the current nutrition landscape – big data and novel technologies
Traditional
Randomized
Controlled Trials
Big Data
Opportunities
Epidemiological
Studies
Scale
Breadth
Depth
Precision
Digital devices
Clinical devices
Remote clinical testing
Citizen science
Largest ongoing program to measure
Individual responses to food in nutritional science
Nutrition academia
Techcompanies
Jun 2018 - May 2019 Jan 2019 - May 2019
n=1,100
PREDICT - Carbs
n=100
Healthy
60% TwinsUK
Healthy Compliant
individuals from
PREDICT 1
Postprandial
responses
Meal order
Time of day
Clinic & Home
(UK/US)
Home (UK)
STATUS:
CompleteSTATUS:
Complete
Validation of remote
study delivery
Postprandial responses
Microbiome profiling
Jun 2019 – Mar 2020
n=1,000
Healthy
Ethnicity
Home (US)
STATUS:
Complete
PREDICT - Cardio
Sep 2019 - Ongoing
n=50
High/Low CVD
risk subset of
PREDICT 1 Plus
Cardiometabolic
end-points
Liver fat
Vascular function
Clinic & Home
(UK)
Continuation of
PREDICT 1
Postprandial
responses
PREDICT 1 Plus
n=900
Jun 2019 - Ongoing
Healthy
TwinsUK
Clinic & Home
(UK)
STATUS:
n=250
Postprandial responses
Dietary assessment
Microbiome profiling
July 2020
n=30,000
Healthy
Ethnicity
Chronic disease
Home (US)
STATUS:
n=5,000
P R E D I C T 2
PREDICT 1
Postprandial
responses
P R E D I C T 3P R E D I C T 1
STATUS:
Complete
https://joinzoe.com/
The PREDICT programme measures the integrated response and interrelated
multi-directional pathways
Controlled Time (Mins)
BLO
OD
Fasting 30 6015 120 180 240 2700 300 360
BLO
OD
BLO
OD
BLO
OD
BLO
OD
BLO
OD
BLO
OD
BLO
OD
BLO
OD
BLO
OD
Questionnaires
FFQ, Lifestyle &
Medical
Anthropometry
DEXA, waist/hip &
BMI
Blood pressure and heart rate
Genetics,
Clinical assays &
Metabolomics
Metabolomics,
Saliva & Urine
SA
LIV
A
SA
LIV
A
Metabolic challenge
Other test meals
Aims
Use genetic, metabolomic, metagenomic and meal-context information to predict individuals’ postprandial responses to food.
Validation Cohort
n=100
Main Cohort
n=1,002
Home Phase (Days 2-14)
Dietary Assessment
Standardisedmeals
Blood Spot tests
Digitaldevices
Stoolsamples
Study app; weighed records; in-study support
Nutritionally varied test breakfasts and lunches
TAG, C-peptide assays
Continuous glucose, physical activity and sleep monitoring
Microbiome profiling
Clinic Day 2 3 4 5 6 7 9 10 13 1411 128
Baseline Clinic Visit (Day 1)
Jun 2018 – May 2019 IRAS 236407 IRB 2018P002078 NCT03479866
2,022,000CGM glucose readings
The scale of the PREDICT 1 study data
32,000Muffins consumed
28,000TAG readings
132,000Meals logged
750,000Metabolomic measures
75 billionMetagenomic reads
Significant variability between healthy individuals
Baseline 6h rise
CV 50% 103%
Triacylglycerol Glucose
Baseline 2h iAUC
CV 10% 68%
Clinic day data, n = 1,002
What are the multiple determinants and how do they impact outcomes?
Meal contextTime of day, Meal
sequence, Exercise, SleepMicrobiome
Glycaemic control
Inflammation
Endothelial dysfunction
Meal composition
Genetics
Microbiome
Age & Sex
Blood pressure
Serum measures
Anthropometry
Habitual diet
Metabolomics
Hunger & energy intake
Exposure Outcome
What
we eat
Who
we are
How
we eat
Serum measures
Anthropometry
Glycaemic control
The determinants differ for different outcomes
Glucose Triglycerides
How can this translate to a real life setting/ dietary advice?
Machine learning
model correlates
77% to measured
glucose responses
Machine Learning model uses test results to predict responses to new meals
Individual takes test
Pearson R = 0.77; p = 0
12
Diet-health-microbiome signature; Personalized gut ‘boosters’and ‘suppressors’
E. lenta
C. leptum
F. plautii
C. bolteae
Escherichia coli
R. lactatiformans
Collinsella intestinalis
Blautia hydrogenotrophica
Clostridium sp. CAG:58
C. symbiosum
C. bolteae CAG 59
A. colihominis
C. innocuum
C. spiroforme
R. gnavus
“Good”
bugs
“Bad”
bugs
E. eligens
B. animalis
F. prausnitzii
H. parainfluenzae
Oscillibacter sp. 57 20
Oscillibacter sp. PC13
Firmicutes bacterium CAG:95
Firmicutes bacterium CAG:170
Roseburia sp. CAG:182
Clostridium sp. CAG:167
Romboutsia ilealis
Veillonella atypica
V. infantium
V. dispar
P. copri
13
The future for the ZOE PREDICT programme
Conflict of interest
Dr Sarah Berry
King’s College LondonDept. Nutritional Sciences
Research grants awarded from; Almond Board of California, Malaysian Health
Ministry, ZOE Global, UK research councils
Consultancy; ZOE Global
